Due to the increased rate of failure of wind generators, condition-monitoring system plays a significant role in overcoming failures resulting from the harsh operation conditions. The mathematical, thermal, and electrical analyses may be utilized to detect the faults of wind generators by monitoring the changes in their characteristics under different operation conditions. The behavior of the rotating permanent magnet of the generator can indicate the wind generator’s condition. For instance, the torque of the permanent magnet of the generator is affected by the oscillation of the magnet temperature. Therefore, monitoring the torque of the permanent magnet with respect to the rate of change in the permanent magnet temperature defines the generator health. Furthermore, the rate of change in the generator temperature is considered an additional indicator to define the health of the wind generators with respect to the induced electrical torque. That is because of the negative effect of the elevated generator temperature on the induced electrical torque. In this study, a different methodology has been adopted to implement a proper condition monitoring system on the wind generators by evaluating the rate of change in the generator temperature and permanent magnet temperature with respect to the induced electrical torque and the

.driving torque of the rotating permanent magnet under different operation conditions.

A case study, which is based upon collected data from actual measurements, is presented in this work in order to demonstrate the adequacy of the proposed model.

This paper presents an application of the hazard model reliability analysis on wind generators, based on a condition monitoring system. The hazard model techniques are most widely used in the statistical analysis of the electric machine’s lifetime data. The model can be utilized to perform appropriate maintenance decision-making based on the evaluation of the mean time to failures that occur on the wind generators due to high temperatures. The knowledge of the condition monitoring system is used to estimate the hazard failure, and survival rates, which allows the preventive maintenance approach to be performed accurately. A case study is presented to demonstrate the adequacy of the proposed method based on the condition monitoring data for two wind turbines. Such data are representative in the generator temperatures with respect to the expended operating hours of the selected wind turbines. In this context, the influence of the generator temperatures on the lifetime of the generators can be determined. The results of the study can be used to develop the predetermined maintenance program, which significantly reduces the maintenance and operation costs. 

Due to the increase in the number of failures in the wind turbine generators, the condition monitoring system plays a significant role in overcoming the negative effects resulting from the difficult operation conditions. Mechanical and electrical properties can be combined to detect the faults coming from wind turbine generators by analyzing their behavior under different (normal and abnormal) operation conditions. Studying the trend and effect of the electrical torque pulsations on wind turbine generators under different conditions allows for a proper condition monitoring. In this paper, different methodology has been adopted to develop a proper condition monitoring system on the wind generators by evaluating the generator electrical torque based on mechanical torque and taking into account the acceleration torque, which has not been considered in previous work. Using the electric torque with respect to the rotor angular speed of the generator, when it is running under different operation conditions, indicates the generator health, which is the main methodology of the proposed work. A case study, which is based upon collected data from actual measurements, is presented in this work in order to demonstrate the adequacy of the proposed model.

Due to the increase in the failures of the wind generators, Condition Monitoring System (CMS) plays a significant role in overcoming these failures resulting from the harsh operation conditions. The mechanical, thermal, and electrical analyses can be utilized to detect the faults, which are coming from the wind generators by monitoring the changes in their characteristics under different (normal and abnormal) operation conditions. Observing the trend of the electrical torque pulsations of the wind generators under different conditions is beneficial to perform proper condition monitoring. In this paper, different methodology has been adopted to implement a proper condition monitoring system on the wind generators by evaluating the generator electrical torque based on the mechanical and the acceleration torque. Then, in order to specify the generator faults, the trend of the electrical torque with respect to the rotor angular speed of the wind generator under different operation conditions is analyzed. Further, the rate of change in the generator temperature is considered as well as an indicator to define the health of the wind generators with respect to the induced electrical torque, because of the negative effect of the elevated generator temperature on the induced electrical torque. Case study, which is based upon collected data from actual measurements, is presented in this work in order to demonstrate the adequacy of the proposed model.

This paper presents an application of a condition-monitoring system (CMS) based on a polynomial regression model (PRM) to study the influence of heat loss on a wind generator’s temperatures. Monitoring the wind generator temperatures is a significant for efficient operation, and plays a key role in an effective CMS. Many techniques, including prediction models can be utilized to reliably forecast a wind generator’s temperature during operation and avoid the occurrence of a failure. PRMs are widely used in situations when the relationship between the response and the independent variables are curve-linear. These techniques can be used to construct a normal behavior model of an electrical generator’s temperatures based on recorded data. Many independent variables affect a generator’s temperature; however, the degree of influence of each independent variable on the response is dissimilar. In many situations, adding a new independent variable to the model may cause unsatisfactory results ;therefore, the selection of the variables should be very accurate. A generator’s heat loss can be considered a significant independent variable that greatly influences the wind generator with respect to the other variables. A generator’s heat loss can be estimated in intervals by analyzing the exchange in the heat between the hot and cold fluid through the heat exchangers of wind generators. A case study built on data collected from actual measurements demonstrates the adequacy of the proposed model. 

Effective cooling is required in power generation, processing, and distribution to avoid failure that could occur during operations. As heat loads continue to increase, manufacturers of wind turbines are turning to improve the cooling system to remove high intensity heat loads from many active parts particularly in the generator part. Wind generators need an effective cooling system due to the large amount of heat that is released during power production. Many large wind turbines (more than 5MW rated power) particularly offshore wind turbines where the water is available, heat exchangers with water-air cooling system is used, in which the water is utilized to cool the hot air. This type of heat exchangers are desired, since they are more efficient and reliable than the air-air heat exchangers, which are used in small wind turbines. Because of the growing number of failures that occurred in wind turbine generators due to high generator’s temperatures owing to power losses of generator, applying condition monitoring system on wind generators depending on heat transfer analysis through the heat exchangers of wind generators plays an effective role. This helps avoid failures and maintain wind turbines to be protected. In this paper new methodology has been applied by considering the heat transfer and fluid mechanics analysis through a heat exchanger of wind generator, which uses water to air cooling system. Case study based on data collected from actual measurements demonstrates the adequacy of the proposed model. 

The development and implementation of condition monitoring system become very important for wind industry with the increasing number of failures in wind turbine generators due to over temperature especially in offshore wind turbines where higher maintenance costs than onshore wind farms have to be paid due to their farthest locations. Monitoring the wind generators temperatures is significant and plays a remarkable role in an effective condition monitoring system. Moreover, they can be easily measured and recorded automatically by the Supervisory Control and Data Acquisition (SCADA) which gives more clarification about their behavior trend. An unexpected increase in component temperature may indicate overload, poor lubrication, or possibly ineffective passive or active cooling. Many techniques are used to reliably predict generator's temperatures to avoid occurrence of failures in wind turbine generators. Multiple Linear Regression Model (MLRM) is a model that can be used to construct the normal operating model for the wind turbine generator temperature and then at each time step the model is used to predict the generator temperature by measuring the correlation between the observed values and the predicted values of criterion variables. Then standard errors of the estimate can be found. The standard error of the estimate indicates how close the actual observations fall to the predicted values on the regression line. In this paper, a new condition-monitoring method based on applying Multiple Linear Regression Model for a wind turbine generator is proposed. The technique is used to construct the normal behavior model of an electrical generator temperatures based on the historical generator temperatures data. Case study built on a data collected from actual measurements demonstrates the adequacy of the proposed model.